Image segmentation for automated taxiing of unmanned aircraft

This paper details a method of detecting collision risks for Unmanned Aircraft during taxiing. Using images captured from an on-board camera, semantic segmentation can be used to identify surface types and detect potential collisions. A review of classifier lead segmentation concludes that texture feature descriptors lack the pixel level accuracy required for collision avoidance. Instead, segmentation prior to classification is suggested as a better method for accurate region border extraction. This is achieved through an initial over-segmentation using the established SLIC superpixel technique with further untrained clustering using DBSCAN algorithm. Known classes are used to train a classifier through construction of a texton dictionary and models of texton content typical to each class. The paper demonstrates the application of said system to real world images, and shows good automated segment identification. Remaining issues are identified and contextual information is suggested as a method of resolving them going forward

Abordagens multiescala para descrição de textura

Orientadores: Hélio Pedrini, William Robson SchwartzDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Visão computacional e processamento de imagens desempenham um papel importante em diversas áreas, incluindo detecção de objetos e classificação de imagens, tarefas muito importantes para aplicações em imagens médicas, sensoriamento remoto, análise forense, detecção de pele, entre outras. Estas tarefas dependem fortemente de informação visual extraída de imagens que possa ser utilizada para descrevê-las eficientemente. Textura é uma das principais propriedades usadas para descrever informação tal como distribuição espacial, brilho e arranjos estruturais de superfícies. Para reconhecimento e classificação de imagens, um grande grupo de descritores de textura foi investigado neste trabalho, sendo que apenas parte deles é realmente multiescala. Matrizes de coocorrência em níveis de cinza (GLCM) são amplamente utilizadas na literatura e bem conhecidas como um descritor de textura efetivo. No entanto, este descritor apenas discrimina informação em uma única escala, isto é, a imagem original. Escalas podem oferecer informações importantes em análise de imagens, pois textura pode ser percebida por meio de diferentes padrões em diferentes escalas. Dessa forma, duas estratégias diferentes para estender a matriz de coocorrência para múltiplas escalas são apresentadas: (i) uma representação de escala-espaço Gaussiana, construída pela suavização da imagem por um filtro passa-baixa e (ii) uma pirâmide de imagens, que é definida pelo amostragem de imagens em espaço e escala. Este descritor de textura é comparado com outros descritores em diferentes bases de dados. O descritor de textura proposto e então aplicado em um contexto de detecção de pele, como forma de melhorar a acurácia do processo de detecção. Resultados experimentais demonstram que a extensão multiescala da matriz de coocorrência exibe melhora considerável nas bases de dados testadas, exibindo resultados superiores em relação a diversos outros descritores, incluindo a versão original da matriz de coocorrência em escala únicaAbstract: Computer vision and image processing techniques play an important role in several fields, including object detection and image classification, which are very important tasks with applications in medical imagery, remote sensing, forensic analysis, skin detection, among others. These tasks strongly depend on visual information extracted from images that can be used to describe them efficiently. Texture is one of the main used characteristics that describes information such as spatial distribution, brightness and surface structural arrangements. For image recognition and classification, a large set of texture descriptors was investigated in this work, such that only a small fraction is actually multi-scale. Gray level co-occurrence matrices (GLCM) have been widely used in the literature and are known to be an effective texture descriptor. However, such descriptor only discriminates information on a unique scale, that is, the original image. Scales can offer important information in image analysis, since texture can be perceived as different patterns at distinct scales. For that matter, two different strategies for extending the GLCM to multiple scales are presented: (i) a Gaussian scale-space representation, constructed by smoothing the image with a low-pass filter and (ii) an image pyramid, which is defined by sampling the image both in space and scale. This texture descriptor is evaluated against others in different data sets. Then, the proposed texture descriptor is applied in skin detection context, as a mean of improving the accuracy of the detection process. Experimental results demonstrated that the GLCM multi-scale extension has remarkable improvements on tested data sets, outperforming many other feature descriptors, including the original GLCMMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

Image segmentation for automated taxiing of Unmanned Aircraft

This paper details a method of detecting collision risks for Unmanned Aircraft during taxiing. Using images captured from an on-board camera, semantic segmentation can be used to identify surface types and detect potential collisions. A review of classifier lead segmentation concludes that texture feature descriptors lack the pixel level accuracy required for collision avoidance. Instead, segmentation prior to classification is suggested as a better method for accurate region border extraction. This is achieved through an initial over-segmentation using the established SLIC superpixel technique with further untrained clustering using DBSCAN algorithm. Known classes are used to train a classifier through construction of a texton dictionary and models of texton content typical to each class. The paper demonstrates the application of said system to real world images, and shows good automated segment identification. Remaining issues are identified and contextual information is suggested as a method of resolving them going forward

TEXTURAL CLASSIFICATION OF MULTIPLE SCLEROSISLESIONS IN MULTIMODAL MRI VOLUMES

Background and objectives:Multiple Sclerosis is a common relapsing demyelinating diseasecausing the significant degradation of cognitive and motor skills and contributes towards areduced life expectancy of 5 to 10 years. The identification of Multiple Sclerosis Lesionsat early stages of a patient’s life can play a significant role in the diagnosis, treatment andprognosis for that individual. In recent years the process of disease detection has been aidedthrough the implementation of radiomic pipelines for texture extraction and classificationutilising Computer Vision and Machine Learning techniques. Eight Multiple Sclerosis Patient datasets have been supplied, each containing one standardclinical T2 MRI sequence and four diffusion-weighted sequences (T2, FA, ADC, AD, RD).This work proposes a Multimodal Multiple Sclerosis Lesion segmentation methodology util-ising supervised texture analysis, feature selection and classification. Three Machine Learningmodels were applied to Multimodal MRI data and tested using unseen patient datasets to eval-uate the classification performance of various extracted features, feature selection algorithmsand classifiers to MRI volumes uncommonly applied to MS Lesion detection. Method: First Order Statistics, Haralick Texture Features, Gray-Level Run-Lengths, His-togram of Oriented Gradients and Local Binary Patterns were extracted from MRI volumeswhich were minimally pre-processed using a skull stripping and background removal algorithm.mRMR and LASSO feature selection algorithms were applied to identify a subset of rankingsfor use in Machine Learning using Support Vector Machine, Random Forests and ExtremeLearning Machine classification. Results: ELM achieved a top slice classification accuracy of 85% while SVM achieved 79%and RF 78%. It was found that combining information from all MRI sequences increased theclassification performance when analysing unseen T2 scans in almost all cases. LASSO andmRMR feature selection methods failed to increase accuracy, and the highest-scoring groupof features were Haralick Texture Features, derived from Grey-Level Co-occurrence matrices

Overcomplete Image Representations for Texture Analysis

Advisor/s: Dr. Boris Escalante-Ramírez and Dr. Gabriel Cristóbal. Date and location of PhD thesis defense: 23th October 2013, Universidad Nacional Autónoma de México.In recent years, computer vision has played an important role in many scientific and technological areas mainlybecause modern society highlights vision over other senses. At the same time, application requirements and complexity have also increased so that in many cases the optimal solution depends on the intrinsic charac-teristics of the problem; therefore, it is difficult to propose a universal image model. In parallel, advances in understanding the human visual system have allowed to propose sophisticated models that incorporate simple phenomena which occur in early stages of the visual system. This dissertation aims to investigate characteristicsof vision such as over-representation and orientation of receptive fields in order to propose bio-inspired image models for texture analysis

Contribuições para a localização e mapeamento em robótica através da identificação visual de lugares

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2015Em robótica móvel, os métodos baseados na aparência visual constituem umaabordagem atractiva para o tratamento dos problemas da localização e mapeamento.Contudo, para o seu sucesso é fundamental o uso de características visuais suficientemente discriminativas. Esta é uma condição necessária para assegurar o reconhecimento de lugares na presença de factores inibidores, tais como a semelhança entre lugares ou as variações de luminosidade. Esta tese debruça-se sobre os problemas de localização e mapeamento, tendo como objectivo transversal a obtenção de representações mais discriminativas ou com menores custos computacionais. Em termos gerais, dois tipos de características visuais são usadas, as características locais e globais. A aplicação de características locais na descrição da aparência tem sido dominada pelo modelo BoW (Bag-of-Words), segundo o qual os descritores são quantizados e substituídos por palavras visuais. Nesta tese questiona-se esta opção através do estudo da abordagem alternativa, a representação não-quantizada (NQ). Em resultado deste estudo, contribui-se com um novo método para a localização global de robôs móveis,o classificador NQ. Este, para além de apresentar maior precisão do que o modeloBoW, admite simplificações importantes que o tornam competitivo, também emtermos de eficiência, com a representação quantizada. Nesta tese é também estudado o problema anterior à localização, o da extracção de um mapa do ambiente, sendo focada, em particular, a detecção da revisitação de lugares. Para o tratamento deste problema é proposta uma nova característica global,designada LBP-Gist, que combina a análise de texturas pelo método LBP com a codificação da estrutura global da imagem, inerente à característica Gist. A avaliação deste método em vários datasets demonstra a viabilidade do detector proposto, o qual apresenta precisão e eficiência superiores ao state-of–the-art em ambientes de exterior.In the mobile robotics field, appearance-based methods are at the core of several attractive systems for localization and mapping. To be successful, however, these methods require features having good descriptive power. This is a necessary condition to ensure place recognition in the presence of disturbing factors, such as high similarity between places or lighting variations. This thesis addresses the localization and mapping problems, globally seeking representations which are more discriminative or more efficient. To this end, two broad types of visual features are used, local and global features. Appearance representations based on local features have been dominated by the BoW (Bag of Words) model, which prescribes the quantization of descriptors and their labelling with visual words. In this thesis, this method is challenged through the study of the alternative approach, the non-quantized representation (NQ). As an outcome of this study, we contribute with a novel global localization method, the NQ classifier. Besides offering higher precision than the BoW model, this classifier is susceptible of significant simplifications, through which it is made competitive to the quantized representation in terms of efficiency. This thesis also addresses the problem posed prior to localization, the mapping of the environment, focusing specifically on the loop closure detection task. To support loop closing, a new global feature, LBP-Gist, is proposed. As the name suggests, this feature combines texture analysis, provided by the LBP method, with the encoding of global image structure, underlying the Gist feature. Evaluation on several datasets demonstrates the validity of the proposed detector. Concretely, precision and efficiency of the method are shown to be superior to the state-of-the-art in outdoor environments

Evaluation of feature descriptors for texture classification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Successful execution of tasks such as image classification, object detection and recognition, and scene classification depends on the definition of a set of features able to describe images effectively. Texture is among the features used by the human visual system. It provides information regarding spatial distribution, changes in brightness, and description regarding the structural arrangement of surfaces. However, although the visual human system is extremely accurate to recognize and describe textures, it is difficult to define a set of textural descriptors to be used in image analysis on different application domains. This work evaluates several texture descriptors and demonstrates that the combination of descriptors can improve the performance of texture classification. (C) 2012 SPIE and IS&T. [DOI: 10.1117/1.JEI.21.2.023016]212Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)FAPESP [2010/10618-3

Analysis Of Brain White Matter Hyperintensities Using Pattern Recognition Techniques

The brain white matter is responsible for the transmission of electrical signals through the central nervous system. Lesions in the brain white matter, called white matter hyperintensity (WMH), can cause a significant functional deficit. WMH are commonly seen in normal aging, but also in a number of neurological and psychiatric disorders. We propose here an automatic method for WHM analysis in order to distinguish regions of interest between normal and non-normal white matter (identification task) and also to distinguish different types of lesions based on their etiology: demyelinating or ischemic (classification task). The method combines texture analysis with the use of classifiers, such as Support Vector Machine (SVM), Nearst Neighboor (1NN), Linear Discriminant Analysis (LDA) and Optimum Path Forest (OPF). Experiments with real brain MRI data showed that the proposed method is suitable to identify and classify the brain lesions. © 2013 SPIE.8669The Society of Photo-Optical Instrumentation Engineers (SPIE),Aeroflex Incorporated,CREOL - Univ. Central Florida, Coll. Opt. Photonics,DQE Instruments, Inc.,Medtronic, Inc.,PIXELTEQ, Multispectral Sensing and ImagingAppenzeller, S., Faria, A.V., Li, L., Costallat, L.T., Cendes, F., Quantitative magnetic resonance imaging analyses and clinical significance of hyperintense white matter lesions in systemic lupus erythematosus patients (2008) Annals of Neurology, 64 (6), pp. 635-643Klöppel, S., Abdulkadir, A., Hadjidemetriou, S., Issleib, S., Frings, L., Thanh, T., Mader, I., Ronneberger, O., A comparison of different automated methods for the detection of white matter lesions in mri data (2011) NeuroImage, 57 (2), pp. 416-422Anbeek, P., Vincken, K.L., Osch, M.J.P., Bisschops, R.H.C., Grond, J., Probabilistic segmentation of whitematter lesions in mr imaging (2004) NeuroImage, 21 (3), pp. 1037-1044Wu, M., Rosano, C., Butters, M., Whyte, E., Nable, M., Crooks, R., Meltzer, C.C., Aizenstein, H.J., A fully automated method for quantifying and localizing white matter hyperintensities on mr images (2006) Psychiatry Research, 148 (2-3), pp. 133-142Zimring, D.G., Achiron, A., Miron, S., Faibel, M., Azhari, H., Automatic detection and characterization of multiple sclerosis lesions in brain mr images (1998) Magnetic Resonance Imaging, 16 (3), pp. 311-318Haralick, R.M., Shanmugam, K., Dinstein, I., Textural features for image classification (1973) , IEEE Transactions on Systems, Man and Cybernetics, 3 (6), pp. 610-621Castellano, G., Bonilha, L., Cendes, F., Texture analysis of medical images (2004) Clinical Radiology, 59 (12), pp. 1061-1069Lerski, R.A., Schad, L., Boyce, D., Blül, S., Zuna, I., Mr image texture analysis: An approach to tissue characterization (1993) Magnetic Resonance Imaging, 11 (6), pp. 873-887Kruggel, F., Paul, J., Gertz, H., Texture-based segmentation of diffuse lesions of the brain's white matter (2008) Neuroimage, 39 (3), pp. 987-996Byun, H., Lee, S.W., Applications of support vector machines for pattern recognition: A survey (2002) Proc. First International Workshop on Pattern Recognition with Support Vector Machines, pp. 213-236Bhatia, N., Survey of nearest neighbor techniques (2010) International Journal of Computer Science and Information Security, 8 (2), pp. 302-305Webb, A.R., (2002) Statistical Pattern Recognition, pp. 123-163. , John Wiley & Sons, MalvernCappabianco, F., Falcão, A., Rocha, L., Clustering by optimum path forest and its application to automatic gm/wm classification in mr-t1 images of the brain (2008) Proc. 5th IEEE International Symposium on Biomedical Imaging: from Nano to Macro, pp. 428-431Lotufo, R., Machado, R., Körbes, A., Ramos, R., Adessowiki: On-line collaborative scientific programming platform (2009) Proc 5th International Symposium on Wikis and Open Collaboration, 10, pp. 1-10. , 6Schwartz, W.R., Siqueira, F.R., Pedrini, H., Evaluation of feature descriptors for texture classification (2012) Journal of Electronic Imaging, 21 (2), pp. 1-17Han, J., Kamber, M., (2006) Data Mining: Concepts and Techniques, pp. 291-310. , Elsevier, San Diego & London & San FransciscoTaylor, J.S., Cristianini, N., (2000) Support Vector Machines and other Kernel-based Learning Methods, pp. 93-122. , Cambridge University Press, New KingdomPapa, J., Falcão, A.X., Suzuki, C.T.N., Supervised pattern classification based on optimum-path forest (2009) International Journal of Imaging Systems and Technology, 19 (2), pp. 120-131Duda, R.O., Hart, P.E., Stork, D.G., (2001) Pattern Classification, , Wiley, Guelph OntarioSouza, R., Rittner, L., Lotufo, R., A comparison between optimum-path forest and k-nearest neighbors classifier (2012) Proc. XXV SIBGRAPI - Conference on Graphics, Patterns and Images, pp. 260-267Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Duchesnay, E., Scikit-learn: Machine learning in python (2011) Journal of Machine Learning Research, 12 (10), pp. 2825-283